--- title: "awesome-gpt vs Awesome-AIGC-Tutorials" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/formulahendry-awesome-gpt-vs-luban-agi-awesome-aigc-tutorials" tools: ["formulahendry-awesome-gpt", "luban-agi-awesome-aigc-tutorials"] --- # awesome-gpt vs Awesome-AIGC-Tutorials *GraphCanon updated Aug 6, 2026* ## Verdict Pick awesome-gpt if awesome-gpt is a curated list of GPT and related resources, serving as a reference for developers exploring or working with large language models and their applications; pick Awesome-AIGC-Tutorials if awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry. [awesome-gpt](https://github.com/formulahendry/awesome-gpt) reports 1.0k GitHub stars, 75 forks, and 27 open issues, last pushed May 29, 2024. [Awesome-AIGC-Tutorials](https://github.com/luban-agi/Awesome-AIGC-Tutorials) has 4.5k stars, 303 forks, and 10 open issues, last pushed Mar 31, 2024. Figures are from public GitHub metadata via [awesome-gpt's repository](https://github.com/formulahendry/awesome-gpt) and [Awesome-AIGC-Tutorials's repository](https://github.com/luban-agi/Awesome-AIGC-Tutorials). | | [awesome-gpt](/tools/formulahendry-awesome-gpt.md) | [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) | | --- | --- | --- | | Tagline | Curated list of GPT and related resources | Curated tutorials and resources for Large Language Models, AI Painting, and more | | Stars | 1,043 | 4,522 | | Forks | 75 | 303 | | Open issues | 27 | 10 | | Language | - | - | | Adopt for | awesome-gpt is a curated list of GPT and related resources, serving as a reference for developers exploring or working with large language models and their applications. | Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry. | | Persona | - | - | | Runtime | - | - | | License | - | MIT license allows for free use in both open-source and proprietary products, with attribution required to the authors. | | Categories | Developer Tools, LLM Frameworks | Developer Tools, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-gpt](/tools/formulahendry-awesome-gpt.md) | [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) | | --- | --- | --- | | Days since push | 799d | 848d | | Open issues (now) | 27 | 10 | | Owner type | User | Organization | | Full report | [trust report](/tools/formulahendry-awesome-gpt/trust.md) | [trust report](/tools/luban-agi-awesome-aigc-tutorials/trust.md) | ## Shared compatibility - **ChatGPT**: [awesome-gpt](/tools/formulahendry-awesome-gpt.md) - Works with ChatGPT; [Awesome-AIGC-Tutorials](/tools/luban-agi-awesome-aigc-tutorials.md) - Works with ChatGPT ## Decision facts: awesome-gpt - **Pricing:** unknown - Information about pricing is unavailable and likely does not apply as this is a curated list rather than a software service with licensing costs. - **Requirements:** Since awesome-gpt is an informational repository, it itself does not have RAM requirements or Docker needs. However, users might require internet access to view - **Adopt for:** awesome-gpt is a curated list of GPT and related resources, serving as a reference for developers exploring or working with large language models and their applications. ## Decision facts: Awesome-AIGC-Tutorials - **Requirements:** No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial. - **Adopt for:** Awesome-AIGC-Tutorials supplies specialized guidance on Large Language Models and AI-generated artistry. - **License detail:** MIT license allows for free use in both open-source and proprietary products, with attribution required to the authors. ## Choose when ### Choose awesome-gpt if… - Pricing: Information about pricing is unavailable and likely does not apply as this is a curated list rather than a software service with licensing costs.. - Requirements: Since awesome-gpt is an informational repository, it itself does not have RAM requirements or Docker needs. However, users might require internet access to view. - Tags unique to awesome-gpt: gpt, openai. - Use awesome-gpt if you are looking for a comprehensive collection of links and resources specifically focused on GPT, ChatGPT, OpenAI products, and other large-scale AI tools. ### Choose Awesome-AIGC-Tutorials if… - Requirements: No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.. - Tags unique to Awesome-AIGC-Tutorials: ai, aigc, deep-learning, midjourney. - Also covers Model Training. - If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources. ## When NOT to use awesome-gpt - Avoid using awesome-gpt if you need detailed tutorials or in-depth technical documentation, as it primarily functions as an index of resources rather than an educational material provider. - Do not rely on awesome-gpt for real-time updates or specific usage statistics, tool availability, or pricing plans since the repository relies heavily on links external to its curation. ## When NOT to use Awesome-AIGC-Tutorials - Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples. - Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis. ## Common questions ### What is the difference between awesome-gpt and Awesome-AIGC-Tutorials? awesome-gpt: Curated list of GPT and related resources. Awesome-AIGC-Tutorials: Curated tutorials and resources for Large Language Models, AI Painting, and more. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-gpt over Awesome-AIGC-Tutorials? Choose awesome-gpt over Awesome-AIGC-Tutorials when Pricing: Information about pricing is unavailable and likely does not apply as this is a curated list rather than a software service with licensing costs.; Requirements: Since awesome-gpt is an informational repository, it itself does not have RAM requirements or Docker needs. However, users might require internet access to view; Tags unique to awesome-gpt: gpt, openai; Use awesome-gpt if you are looking for a comprehensive collection of links and resources specifically focused on GPT, ChatGPT, OpenAI products, and other large-scale AI tools. ### When should I choose Awesome-AIGC-Tutorials over awesome-gpt? Choose Awesome-AIGC-Tutorials over awesome-gpt when Requirements: No specific technical prerequisites are listed. Basic understanding of AI concepts like LLMs and NLP is beneficial.; Tags unique to Awesome-AIGC-Tutorials: ai, aigc, deep-learning, midjourney; Also covers Model Training; If you aim to deepen your understanding of prompt engineering for models like MidJourney or Stable Diffusion, this repository offers focused tutorials and resources. ### When should I avoid awesome-gpt? Avoid using awesome-gpt if you need detailed tutorials or in-depth technical documentation, as it primarily functions as an index of resources rather than an educational material provider. Do not rely on awesome-gpt for real-time updates or specific usage statistics, tool availability, or pricing plans since the repository relies heavily on links external to its curation. ### When should I avoid Awesome-AIGC-Tutorials? Avoid if you are looking for a one-stop-shop coding platform, as Awesome-AIGC-Tutorials provides theoretical knowledge and tutorials rather than practical code samples. Not suitable if your focus is solely on the commercial deployment of large language models; this repository does not cover market-specific insights or competitive analysis. ### Is awesome-gpt or Awesome-AIGC-Tutorials more popular on GitHub? Awesome-AIGC-Tutorials has more GitHub stars (4,522 vs 1,043). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-gpt and Awesome-AIGC-Tutorials open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-gpt or Awesome-AIGC-Tutorials? GraphCanon lists graph-backed alternatives at [awesome-gpt alternatives](/tools/formulahendry-awesome-gpt/alternatives) and [Awesome-AIGC-Tutorials alternatives](/tools/luban-agi-awesome-aigc-tutorials/alternatives) ([awesome-gpt markdown twin](/tools/formulahendry-awesome-gpt/alternatives.md), [Awesome-AIGC-Tutorials markdown twin](/tools/luban-agi-awesome-aigc-tutorials/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/formulahendry-awesome-gpt-vs-luban-agi-awesome-aigc-tutorials.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-gpt or Awesome-AIGC-Tutorials? awesome-gpt: Dormant. Awesome-AIGC-Tutorials: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for awesome-gpt and Awesome-AIGC-Tutorials? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-gpt trust report](/tools/formulahendry-awesome-gpt/trust); [Awesome-AIGC-Tutorials trust report](/tools/luban-agi-awesome-aigc-tutorials/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=formulahendry-awesome-gpt`](/api/graphcanon/graph?tool=formulahendry-awesome-gpt) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_